The 60-second answer
An LLM is typically a large Transformer trained to model token sequences, most commonly by predicting the next token from previous context. Pretraining learns broad statistical representations from large corpora; instruction tuning/preferences can adapt behavior for conversational or task use.
Build the answer in this order
An LLM is typically a large Transformer trained to model token sequences, most commonly by predicting the next token from previous context.
Pretraining learns broad statistical representations from large corpora; instruction tuning/preferences can adapt behavior for conversational or task use.
At inference the model repeatedly produces a probability distribution over the next token and decodes according to a sampling/selection strategy.
Separate model knowledge from external grounding: tools/RAG can add current/private evidence without changing model weights.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers distinguish pretraining, supervised fine-tuning, preference optimization, and inference-time prompting/RAG.
- Include context-window, latency, cost, hallucination, and evaluation constraints rather than defining an LLM only by parameter count.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.